This README lists R files and data needed to replicate the figures and tables in the main text and supporting information of "Offsetting Policy Feedback Effects: Evidence from the Affordable Care Act".

Some data cannot be provided due to data-sharing restrictions. In those cases, we have provided the code needed to replicate the analyses. In particular, data from the KFF surveys must now be accessed from the Roper archives: https://ropercenter.cornell.edu/ipoll/ or https://ropercenter.cornell.edu/kaiser-family-foundation. Geo-coded data require access to restricted data and interested researchers must request this data from KFF directly (https://www.kff.org).

Please contact the authors with any questions. We're happy to answer questions and we appreciate others double-checking the work.

File structure:
./code/
./data/
./figs/

FIGURES and TABLES

**** Main text ****

- Figure 1: Personal benefit or harm attributed to the ACA, 2010-2014.
    : health_survey_load_helped_hurt.R
    : KFF data must be accessed through Roper

- Figure 2: ACA attitudes for respondents to the 2012-2018 panel surveys who completed the November/December 2016 and October 2018 waves conducted through GfK’s Knowledge Panel.
    : figure2-panel-aca-replication-12112020.R
    : data: "data/figure2-panel-aca-replication-12112020.Rdata"

- Table 2: Uninsured difference-in-difference.
    : table2-panel-12112020.R
    : data: "data/table2-panel-aca-replication-12112020.Rdata"

- Table 3: Changes in exchange prices and ACA attitudes. Note: researchers wishing to replicate this analysis must obtained geographically restricted Health Tracking Survey survey data from Kaiser Family Foundation
    : table3-01-HIX-merge-12112020.R
    : table3-02-kff-exchanges-12142020.R
    : KFF data must be requested from KFF
    : data: "/exchangedata/Working ACAGeography Counties & Ratings Area12202018.csv"
    : data: "/exchangedata/summary_ratingarea_2014.csv"
    : data: "/exchangedata/summary_ratingarea_2015.csv"
    : data: "/exchangedata/summary_ratingarea_2016.csv"
    : data: "/exchangedata/summary_ratingarea_2017.csv"
    : data: "/censusdata/SocialExplorer-2016ACS-5y-CountyData-R11818880_SL050.txt"
    : data: "/censusdata/SocialExplorer-2016ACS-5y-CountyMobility-R11818891_SL050.txt"
    : data: "/censusdata/SocialExplorer-2010ACS-5y-CountyData-R11818885_SL050.txt"
    : data: "/censusdata/SocialExplorer-2000Census-2010Geographies-CountyData-R11818901_SL050.txt"
    : data: "/exchangedata/Working ACAGeography Counties & Ratings Area12202018.csv"

- Figure 3: ACA attitudes for 64 year olds compared to 65 year olds.
    : age_rdd_analyses.R
    : KFF data must be accessed through Roper
    : data: "data/kff_main_replication_data.RData" (produced by prep_kff_replication_data_subset.R, original data must be obtained from Roper)

- Figure 4: RDD model figure
    : age_rdd_analyses.R
    : KFF data must be accessed through Roper
    : data: "data/kff_main_replication_data.RData"

- Figure 5: Over-time associations between insurance source scores, ACA attitudes.
    : insurance_source_score_*
    : KFF data must be accessed through Roper
    : data: "data/kff_main_replication_data_with_psraid.RData"


**** Supporting information ****

- Figure A1: Distribution of HTS respondents by health insurance source before and after the ACA’s January 2014 implementation.
    : insurance_source_score_*

- Table A1: Key covariates and their associations with insurance sources, respondents to surveys after December 2013.
    : insurance_source_score_*

- Figure A2: Dichotomized ACA favorability by health insurance status and source over time.
    : favorability_by_insurance_source_kff.R
    : KFF data must be accessed through Roper
    : data: "data/kff_main_replication_data.RData"

- Figure A3: Actual versus expected favorability by insurance source.
    : insurance_source_score_*

- Figure A4: Difference between each group’s actual ACA favorability and its expected ACA favorability given its demographics multiplied by its overall population size.
    : insurance_source_score_*

- Figure A5: Helped or Hurt by the ACA, by Insurance Source.
    : helped_hurt_by_insurance_source_kff.R

- Table A2: Numbers of post-2014 respondents by insurance source: personally benefited from or negatively affected by ACA survey question.
    : health_survey_helped_hurt_text.R

- Table A3: Open-ended keywords, ACA harm – uninsured.
    : health_survey_helped_hurt_text.R

- Table A4: Open-ended keywords, ACA benefit – self-purchased insurance.
    : health_survey_helped_hurt_text.R

- Table A5: Prior insurance sources for respondents in the KFF 2014 non-group survey.
    : nongroup_analyses_kff.R
    : KFF data must be accessed through Roper
    : data: Wave 1 2014/N1008_2.sav
    : data: "Wave 2 2015/n1124.sav"
    : data: "Wave 3 2016/o1103_client 040816.sav"

- Table A6: Models of ACA favorability among the non-group insured, surveyed by KFF in 2014- 2016 (full models).
    : nongroup_analyses_kff.R

- Table A7: OLS models of respondents to the 2015 Kentucky KFF survey (full model).
    : kentucky_kynect_analysis.R
    : KFF data must be accessed through Roper
    : data: "KFF_KentuckySurvey_Data_12.04.15.sav"

- Table A8: ACA favorability model using demographics only, HTS data pre-implementation.
    : insurance_source_score_*

- Table A9: Panel wave summaries and sample sizes.

- Table A10: Demographics for panel waves.

- Table A11: Coefficients from a change score or ``difference-in-difference'' model of the change in respondents' support for repealing the ACA, measured on a 1-7 scale, between fall 2016 and fall 2018.
    : table2-panel-12112020.R

- Table A12: OLS model of fall 2018 anti-ACA attitudes, measured on a 1-7 scale, as a function of various variables.
    : table2-panel-12112020.R

- Table A13: OLS model of fall 2018 anti-ACA attitudes, measured on a 1-7 scale, as a function of various variables.
    : table2-panel-12112020.R

- Figure A6: Coefficients for being uninsured in 2016 and 2018 (included in the same model) when predicting 2018 support for ACA repeal using the [name redacted] panel.
    : table2-panel-12112020.R

- Table A14: Results of the full multi-level models fit to KFF respondents from 2015, 2016, and 2017 in which certain insurance market conditions (and various other independent variables) predict ACA favorability, measured on a 1 to 4 scale.
    : table3-02-kff-exchanges-12142020.R

- Figure A7: Fraction of respondents to KFF open-ended questions about the ACA mentioning Medicare over time.
    : plot_medicare.R

- Table A15: RDD models fit to respondents between 62 and 68 before (left) and after (right) the ACA’s implementation.
    : age_rdd_analyses.R

- Table A16: Checks of the key assumption underpinning the RDD analyses, which is that potentially confounding variables are distributed smoothly at the point of the discontinuity.
    : age_rdd_analyses.R

- Table A17: Insurance source score models.
    : insurance_source_score_*

- Table A18: Summary statistics for insurance source scores.
    : insurance_source_score_*

- Table A19: Insurance source score correlations.
    : insurance_source_score_*

- Figure A8: Over-time associations between insurance source scores (exchanges), ACA attitudes.
    : insurance_source_score_*

- Table A20: Results of a multi-level model predicting four-category ACA favorability.
    : insurance_source_score_*

- Table A21: Insurance source score models for interpretation (Poisson GLM, coefficients as risk ratios, no polynomials).
    : insurance_source_score_*

- Table A22: Association between the uninsured scores and future insurance status in the [redacted] panel using the coefficients from the KFF HTS model.
    : insurance_source_score_*

- Table A23: Associations between the uninsured score financial burden in the KFF HTS.
    : insurance_source_score_*

- Table A24: Association between the uninsured score and responses indicating feeling vulnerable to high medical bills in the KFF HTS.
    : insurance_source_score_*

- Table A25: Association between the self-insured scores and future insurance status in the [redacted] panel using the coefficients from the KFF model.
    : insurance_source_score_*

- Figure A9: Partisanship & Insurance Status Scores.
    : insurance_source_score_*

- Figure A10: Over-time associations between insurance source scores, partisan identification.
    : insurance_source_score_*

- Figure A11: Over-time associations between insurance source scores, identification as independent.
    : insurance_source_score_*

- Table A26: Model results for RDD with party ID as DV.
    : age_rdd_analyses.R
